This paper investigates latency-constrained resource synergization for mission-oriented non-terrestrial networks (NTNs) in post-disaster emergency scenarios. When terrestrial infrastructures are damaged, unmanned aerial vehicles (UAVs) equipped with edge information hubs (EIHs) are deployed to provide temporary coverage and synergize communication and computing resources for rapid situation awareness. We formulate a joint resource configuration and location optimization problem to minimize overall resource costs while guaranteeing stringent latency requirements. Through analytical derivations, we obtain closed-form optimal solutions that reveal the fundamental tradeoff between communication and computing resources, and develop a successive convex approximation method for EIH location optimization. Simulation results demonstrate that the proposed scheme achieves approximately 20% cost reduction compared with benchmark approaches, validating its optimality and effectiveness for mission-critical emergency response applications in the sixth-generation (6G) era.

In industrial automation or emergency rescue, sensors and robots work together with the help of an edge information hub (EIH) containing both communication and computing modules. Typically, the EIH collects the sensing data via the sensor-to-EIH link, processes data and then makes decisions on board before sending commands to the robot via the EIH-to-robot link. This forms a sensing-communication-computing-control (SC3) closed loop. In practice, the inherent openness of wireless links within the closed loop leads to susceptibility to eavesdropping. To this end, this paper refines the conventional physical layer security (PLS) approach with a systematic thinking to safeguard the SC3closed loop. The closed-loop negentropy (CNE), a new metric for the performance of the whole SC3closed loop, is maximized under the closed-loop security constraint. The transmit time, power, bandwidth of both wireless links, and the computing capability, are jointly designed. The optimization problem is non-convex. We leverage the Karush-Kuhn-Tucker (KKT) conditions and the monotonic optimization (MO) theory to derive its globally optimal solution. Simulation results show the performance gain of the proposed systematic approach, and reveal the advantage of exploiting the closed-loop structure-level PLS over the link-level or sum-link-level designs.

Future sixth generation (6G) communications are expected to support robotic control tasks in applications such as industrial automation and emergency response, where sensors, computing units, and robots are interconnected via nervous system-like networks to form sensing-communication-computingcontrol (SC 3 ) closed loops.However, the limited battery capacities of devices within these SC 3 loops constrain operational duration and degrade control efficiency, particularly in remote or postdisaster scenarios.To address this challenge, wireless power transfer (WPT) can be leveraged to provide continuous energy supply for SC 3 closed loops.In this paper, we investigate a wireless-powered SC 3 system, where a satellite transfers energy via radio frequency (RF) signals to support the communication and computing processes of multiple SC 3 closed loops.By accounting for the intricate coupling among computing, communication, and energy transfer, we propose a holistic design framework to enhance overall control performance.Specifically, we adopt the linear quadratic regulator (LQR) cost as the performance metric and formulate a sum LQR cost minimization problem.The uplink/downlink transmit power, bandwidth allocation, computing capability, communication/computing time allocation, and WPT power allocation are jointly optimized.We recast the problem into a more tractable form and develop an iterative algorithm to solve it.For the special case of a single loop, we further analyze the properties of optimal solutions in energylimited scenarios to provide insights for practical parameter configuration.Simulation results demonstrate the performance gains of the proposed scheme.

Multi-hop networks are vital for establishing emergency communications, in case the terrestrial communication infrastructures are compromised. Organizing nodes into specific structures can enhance system resilience and efficiency. To this end, we consider using directional antennas to enhance transmission, and the nodes form a Z-chain structure based on the location information provided by the satellite. The core mechanism for performance gain lies in the spatial separation that shifts the dominant interference from the high-gain antenna main lobes to the attenuated side lobes. An exhaustive search demonstrates the optimality of the Z-chain configuration. To combat performance degradation from antenna pointing errors and node drift, we introduce an alternating Newton method (ANM) that jointly optimizes network structure and beam configurations by minimizing single-hop outage probability. Simulations show that the proposed Z-chain structure surpasses existing linear relay designs by converting most intra-chain interference from main-lobe to side-lobe directions, albeit with additional relay nodes. The Z-chain structure offers a promising paradigm for high-performance wireless networks with potential applications in terrestrial, aerial, and space communications.

Directional antennas can synergize with suitable network structure to complement each other in multi-hop wireless networks. This work addresses the combination of directional transmission and network structure to improve the capacity. Firstly, Z-chain structure considering the antenna radiation pattern is proposed, which is consistent with the best chain structure obtained by exhaustive search. Secondly, theoretical analysis indicates that spectral efficiency$\eta_{S}$behaves as a sigmoid function of node density$\rho$, with its asymptotic value increasing as$\Theta\left[\log \left(G_{r} \sin ^{2} \theta\right)\right]\left(\theta \leq \theta^{*}\right)$for large values of$G_{r}$, where$G_{r}$denotes the relative antenna gain,$\theta^{*}$is a function of the antenna beam width and$2 \theta$is the$\mathbf{Z}$-chain link angle. Thirdly, energy efficiency and consumption is assessed from a traffic load perspective. The findings reveal that the Z-chain network can accommodate higher data traffic compared to conventional frequency reuse method and straight structure. Experiments conducted using OMNeT++ validate above conclusions.

Providing direct-to-cell (D2C) services via non-geostationary orbit (NGSO) satellites constitutes one of the critical applications in satellite communications for non-terrestrial networks (NTN). To mitigate frequent beam handovers caused by the high mobility of NGSO satellites, an earth-fixed service mode can be employed to maintain beam staring at target areas. Addressing the capacity degradation due to delayed beam updates, we propose a beam tracking method based on dual codebook interpolation. This approach estimates beam update intervals through beam pattern analysis and angular velocity of terminals within satellite’s field of view, while maintaining the codebook. The precoder at any instant is generated through linear interpolation between active and updated codebooks. Simulation results demonstrate that the proposed method adaptively adjusts beam update intervals according to terminal’s position and channel conditions, effectively reducing beam tracking frequency while maintaining system capacity above specified thresholds.

Relay nodes (RNs) enhance the connectivity and coverage of Wireless Sensor Networks (WSNs), but their deployment remains an NP-hard problem. This work incorporates directional antennas to improve network performance while addressing the analytical and computational challenges they introduce. We adopt the Rician fading model to capture realistic wireless channel conditions, including path loss, fading, and interference, enabling accurate signal-to-interference-plus-noise ratio (SINR) estimation. Outage probability is embedded into edge weights for relay placement optimization. To solve this problem, we formulate the constrained relay node placement problem, prove its NP-hardness, and develop a polynomial-time approximation algorithm that balances minimizing outage probability and limiting relay node deployment. Comparative analysis with existing algorithms shows that our method significantly reduces outage probability and improves network performance while keeping relay deployment costs within a reasonable range.

The software-defined wide area network (SD-WAN) has emerged as a promising architecture for the next-generation WANs, where the Internet service provider (ISP) leases network resources from the infrastructure provider (InP) and offers various applications through service function chaining. Taking the perspective of an ISP, this study focuses on two closely coupled issues. 1) Resource Allocation (RA): In bandwidth-constrained SD-WANs, network resources need to be flexibly allocated according to link congestion status. 2) Resource Provisioning (RP): Due to service dynamics, ISPs need to adjust their resource leasing schemes in response to request variations. The joint consideration of RA and RP enables bidirectional adaptation between the network and services. In this work, an ILP problem is formulated to unify RA and RP within one framework. For RA, a congestion-aware heuristic algorithm is proposed to achieve load balancing through probabilistic dispersion of data flows across different requests. For RP, a Bayesian Optimization-based algorithm is designed to facilitate resource capacity planning for ISPs. Simulations demonstrate that the proposed methods can reduce request rejection by 50% in bandwidth-constrained networks, improve resource utilization efficiency, enhance the ISP’s benefit, and mitigate performance fluctuations caused by service dynamics.

In recent decades, cyber-physical systems (CPSs) have received great attention due to their broad applications. This paper investigates CPS deployment in remote areas, specifically focusing on a digital twin-empowered unmanned robotic system. The system consists of a multifunctional unmanned aerial vehicle (UAV), sensors, and actuators. The UAV carries communication and computing modules, acting as an edge information hub (EIH) that connects sensors and actuators—forming reflex-arc-like sensing-communication-computing-control (SC3) loops. A digital twin is integrated into the EIH to emulate the system’s behavior and assist in the decision-making. To alleviate resource limitations in remote areas, we propose a goal-oriented closed-loop optimization scheme. The proposed scheme takes the SC3loop as an integrated structure and jointly optimizes uplink and downlink (UL&DL) communication and computing resources to minimize the total linear quadratic regulator (LQR) cost. To address the non-convex optimization problem, we derive the closed-form solution for intra-loop allocation and propose an efficient iterative algorithm for inter-loop optimization. Under the condition of adequate CPU frequency, we derive an approximate closed-form solution for inter-loop bandwidth allocation. Simulation results demonstrate the superiority of the proposed scheme, which achieves a two-tier task-level balance within and across the SC3loops.

An increasing number of field robots would be used for mission-critical tasks in remote or post-disaster areas. Due to the limited individual abilities, these robots usually require an edge information hub (EIH), with not only communication but also sensing and computing functions. Such EIH could be deployed on a flexibly-dispatched unmanned aerial vehicle (UAV). Different from traditional aerial base stations or mobile edge computing (MEC), the EIH would direct the operations of robots via sensing-communication-computing-control ($\textbf {SC}^{3}$) closed-loop orchestration. This paper aims to optimize the closed-loop control performance of multiple$\textbf {SC}^{3}$loops, with constraints on satellite-backhaul rate, computing capability, and on-board energy. Specifically, the linear quadratic regulator (LQR) control cost is used to measure the closed-loop utility, and a sum LQR cost minimization problem is formulated to jointly optimize the splitting of sensor data and allocation of communication and computing resources. We first derive the optimal splitting ratio of sensor data, and then recast the problem to a more tractable form. An iterative algorithm is finally proposed to provide a sub-optimal solution. Simulation results demonstrate the superiority of the proposed algorithm. We also uncover the influence of$\textbf {SC}^{3}$parameters on closed-loop controls, highlighting more systematic understanding.

Motion in-betweening that aims to generate motion transitions between known keyframes plays a significant role in the 3D character animation industry. However, generating long-term transitions is highly challenging due to the non-stationary nature and considerable spatio-temporal uncertainty of motions. Leading transformer-based methods operate at a single temporal scale while they overlook the spatial interactions among joints and temporally hierarchical structure of motions, leading to the generation of over-smoothed and weak transitions. In this paper, we propose a novel spatio-temporal framework for the motion in-betweening task. First, a spatial transformer is introduced to capture the per-frame spatial dependencies among joints, enhancing the capability of the model to generalize across diverse action types. Furthermore, to alleviate over-smoothing, a multi-scale temporal transformer is designed to generate dynamic and realistic transitions by capturing the hierarchical structure of motions, which includes both global motion trends and local subtle variations. Extensive experiments on the LAFAN1 dataset demonstrate that our method achieves state-of-the-art performance compared to existing methods. In addition, the corresponding ablation studies and sensitivity analyses verify the effectiveness of the proposed spatio-temporal framework.

In emergency rescue scenarios, field robots can be dispatched to enhance rescue operations, and unmanned aerial vehicles (UAVs) can be utilized to serve field robots thanks to their flexibility and on-demand deployment. To support the robots efficiently, UAVs should be equipped with sensors, base stations (BSs), and mobile edge computing (MEC) servers. The whole process of a typical rescue task can be regarded as a sensing-communication-computing-control (SC3) closed loop. In this paper, we focus on the closed-loop performance of SC3loops, which is essential for mission-critical tasks. Specifically, we propose a joint communication and computing resource allocation problem, aiming to minimize the sum linear quadratic regulator (LQR) cost of SC3loops. We prove the convexity of the optimization problem by introducing auxiliary variables. Numerical results are provided to show that our proposed scheme can enhance the system’s control performance. Our work also shows that it is essential to jointly consider the communication, computing, and sensing capabilities in unmanned rescue tasks.

Non-terrestrial networks (NTNs) using flying objects such as satellites play key roles in the next-generation wireless system (6G). The NTN links with long propagation delay and random packet losses pose a great challenge to the performance of Transmission Control Protocol (TCP), which many Internet applications rely on. Performance enhancing proxy (PEP) is an easy-to-deploy approach for improving TCP's performance. In this paper, we design and implement a novel PEP calledPEPescwhich has two distinctive features. First, it featuresretransmission-freeloss recovery, using an adaptive packet-level forward erasure correction method called streaming coding (SC). Second, as packet losses are recovered by SC, the congestion control problem is simplified to rate control and local acknowledgement between entities based on bandwidth estimation. Based on a queueing theoretic analysis of the design, we carefully devise a protocol and implement PEPesc as an open-source application. Extensive evaluations show that PEPesc can achieve much higherandsmoother goodput than the canonical TCP variants and than other existing open-source PEPs in applications includingiperfand HTTP-based adaptive streaming, and achieves similar performance in web browsing. Finally, we also present a deployment case over a real-world geostationary satellite link.

In unmanned space exploration, the cooperation among space robots requires advanced communication techniques. In this paper, we propose a communication optimization scheme for a specific cooperation system named the “mother-daughter system”. In this setup, the mother spacecraft orbits the planet, while daughter probes are distributed across the planetary surface. During each control cycle, the mother spacecraft senses the environment, computes control commands and distributes them to daughter probes for actions. They synergistically form sensing-communication-computing-control ($\mathbf {SC^{3}}$) loops. Given the indivisibility of the$\mathbf {SC^{3}}$loop, we optimize the mother-daughter downlink for closed-loop control. The optimization objective is the linear quadratic regulator (LQR) cost, and the optimization parameters are the block length and transmit power. To solve the nonlinear mixed-integer problem, we first identify the optimal block length and then transform the power allocation problem into a tractable convex problem. We further derive the approximate closed-form solutions for the proposed scheme and two communication-oriented schemes: the max-sum rate scheme and the max-min rate scheme. On this basis, we analyze their power allocation principles. In particular, for time-insensitive control tasks, we find that the proposed scheme demonstrates equivalence to the max-min rate scheme. These findings are verified through simulations.

This paper investigates an emergency rescue system, which comprises a multi-functional unmanned aerial vehicle (UAV) and multiple robots. The UAV carries sensing, communication, and computing modules. It senses system states, calculates commands, and instructs field robots to take actions. In this way, the UAV and robots form multiple sensing-communication-computing-control $(\mathbf{SC} ^{3})$ loops, which could finish many mission-critical tasks without human participation. To activate these $\mathbf{SC} ^{3}$ loops, we propose a closed-loop optimization scheme. Unlike traditional studies that primarily focus on the communication link, the proposed scheme emphasizes the $\mathbf{SC} ^{3}$ loop and adopts the linear quadratic regulator (LQR) cost as the objective. Focusing on the UAV-robot downlink, we model the data transmission in the finite block length regime and take the transmit power and block lengths as optimization variables. We solve the nonlinear integer problem by exploiting the monotonicity and convexity of the objective rate-cost function. The closed-form solution of the transmit power is derived in the assure-to-be-stable region. On this basis, we compare the proposed scheme with the max-sum rate scheme. Through comparisons, the fairness-minded nature of the proposed scheme is revealed.

In this paper, we address the service function chaining (SFC) problem for software-defined wide area networks (SD-WANs). Due to its NP-hardness, the service chaining problem is simplified by most existing works to apply to practical-size networks. However, in SD-WAN scenarios, these simplifications may lead to link congestion due to the heterogeneity of geographically distributed networks. Different from previous studies, we propose a congestion-aware algorithm, where more comprehensive and practical constraints are considered, and load balancing is introduced to reduce link congestion. First we formulate an integer linear programming model for exact solution, then the original problem is simplified and heuristic algorithms are designed to deploy the SFC requests in batches. Specifically, load balancing is implemented by updating the routing rules according to network congestion status, and data flows from different requests are dispersed in a probabilistic manner, which to our knowledge is first adopted for load balancing in SFC problems. Simulation results show that our algorithm can significantly reduce link congestion, thus improving the acceptance rate of SFC requests and obtaining higher benefits.

Network-enabled robots have become important to support future machine-assisted and unmanned applications. To provide high-quality services for wide-area robots, hybrid satellite-terrestrial networks are a key technology. Via hybrid networks, computation-intensive and latency-sensitive tasks of robots can be offloaded to mobile edge computing (MEC) servers. However, due to the mobility of mobile robots and unreliable wireless network environments, excessive local computations and frequent service migrations may significantly increase the service delay. To address this issue, this paper aims to minimize the average task completion time for MEC-based offloading for satellite-terrestrial-network-enabled robots. Different from conventional mobility-aware schemes, the proposed scheme is to make the offloading decision by jointly considering the mobility control of robots. A joint optimization problem of task offloading and velocity control is formulated. Using Lyapunov optimization, the original optimization is decomposed into a velocity control subproblem and a task offloading subproblem. Then, based on the Markov decision process (MDP), a dual-agent reinforcement learning (RL) algorithm is proposed. Simulation results show that the proposed scheme can effectively reduce the service delay.

While 5G networks have accelerated the development of the Internet of Things (IoT), they have also introduced a large number of vulnerable IoT devices into the network, which would lead to severe Distributed Denial-of-Service (DDoS) attacks. The newly emerging DDoS attack methods generally have a shorter duration, which imposes higher requirements for the response time of DDoS mitigation technologies. Existing DDoS defense methods cannot achieve real-time detection due to the difficulty of reducing the delay of feature extraction and large-scale data processing. In this article, we focus on the timeliness of DDoS detection and mitigation. We hope that deploying effective defense countermeasures at the source side will block the majority of DDoS attack traffic in real time before it enters the data network (DN). To this end, we propose a real-time DDoS defense framework based on multidomain collaboration that combines multisource information to detect attack sessions with high accuracy in 5G networks. To operate the framework at line rate, we propose an optimal packet sampling strategy based on the accurate session size estimation, which can greatly reduce the detection overhead while ensuring good accuracy. In a typical scenario with an attack session size larger than 10, this method can achieve a 99% detection rate while reducing the packet inspection rate (PIR) to less than 37%.

The sixth-generation (6G) network is envisioned to integrate communication and sensing functions, so as to improve the spectrum efficiency and support explosive novel applications. Although the similarities of wireless communication and radio sensing lay the foundation for their combination, there is still considerable incompatible interest between them. To simultaneously guarantee the communication capacity and the sensing accuracy, the multiple-input and multiple-output (MIMO) technique plays an important role due to its unique capability of spatial beamforming and waveform shaping. However, the configuration of MIMO also brings high hardware cost, high power consumption, and high signal processing complexity. How to efficiently apply MIMO to achieve balanced communication and sensing performance is still open. In this survey, we discuss joint communication and sensing (JCAS) in the context of MIMO. We first outline the roles of MIMO in the process of wireless communication and radar sensing. Then, we present current advances in both communication and sensing coexistence and integration in detail. Three novel JCAS MIMO models are subsequently discussed by combining cutting-edge technologies, i.e., cloud radio access networks (C-RANs), unmanned aerial vehicles (UAVs), and reconfigurable intelligent surfaces (RISs). Examined from the practical perspective, the potential and challenges of MIMO in JCAS are summarized, and promising solutions are provided. Motivated by the great potential of the Internet of Things (IoT), we also specify JCAS in IoT scenarios and discuss the uniqueness of applying JCAS to IoT. In the end, open issues are outlined to envisage a ubiquitous, intelligent, and secure JCAS network in the near future.

Due to the heterogeneity of Internet of Things (IoT) devices and the diversity of IoT communication protocols, it is challenging to model the communication behaviors of IoT devices to facilitate attack defense. Considering the complex correlation between the IoT device types and the patterns of their communication behaviors, one possible solution is to cluster IoT devices into different types based on the characteristics of their communication behaviors and deal with each type, respectively. However, IoT traffic includes a significant proportion of abnormal traffic, such as attack traffic sourcing from compromised devices, which cannot reflect the behavioral characteristics of the source device. In this article, we propose a Transformer-based IoT device-type identification method to address the above challenges. Specifically, our approach consists of three main components. First, we classify the traffic data from IoT devices into normal and abnormal types by a Transformer-based traffic diagnosis model. Next, another Transformer-based model is adopted on the normal traffic to identify the IoT device type. Finally, considering the immutability of IoT device types, a results-ensemble algorithm is designed to improve the accuracy of IoT device-type identification. Experimental results verify the effectiveness of our method, which brings a noticeable improvement in terms of both accuracy and macro$F1$-score compared to other methods. Moreover, by applying the results-ensemble algorithm in the test phase, we can achieve 100% accuracy under certain conditions.

3D human pose estimation has a broad range of applications, including anomaly detection and animation creation. Despite that significant progress on relative research has been made during the past decades, producing precise and smooth estimations for input videos still remains challenging mainly because of its ill-posed attributes. In this paper, we propose HiMoReNet, a post-processing motion refinement neural network based on an elaborate hierarchical architecture. Firstly, we distinguish characteristic motion patterns of joints at different locations by grouping the joints and employing respective spatiotemporal processing modules for each group. In addition, by mimicking interactions among multiple body parts, global context information is leveraged to further guide the motion refinement. Quantitative and qualitative results on the 3DPW dataset demonstrate that our proposed HiMoReNet achieves the state-of-the-art performance, and excels in jitter removal and precise pose estimation.

Wideband extremely large-scale multiple-input-multiple-output (XL-MIMO) plays an important role in boosting the data rate for 6G networks. Because of the huge bandwidth and the large number of antennas, wideband XL-MIMO introduces a significant near-field beam split effect, where beams at different frequencies are focused on different locations. This effect results in a severe array gain loss, and existing works mainly consider to compensate for this loss by utilizing time-delay (TD) beamforming. This paper demonstrates that despite degrading the array gain, the near-field beam split effect can also contribute to the fast near-field beam training. Specifically, we first reveal the controllable near-field beam split effect. This effect indicates that TD beamforming can control the degree of the near-field beam split effect, i.e., beams at different frequencies can flexibly occupy the desired location range. Due to the similarity with the dispersion of natural light caused by a prism, we also call this effect as “near-field rainbow”. Then, by taking advantage of the near-field rainbow, a fast wideband beam training scheme is proposed to generate beams focusing on multiple locations at multiple frequencies with the help of TD beamforming. Finally, simulation results demonstrate that the proposed scheme is able to realize efficient near-field beam training with low training overheads.

Current fifth-generation (5G) networks do not cover maritime areas, causing difficulties in developing maritime Internet of Things (IoT). To tackle this problem, we establish a nearshore network by collaboratively using on-shore terrestrial base stations (TBSs) and tethered unmanned aerial vehicles (UAVs). These TBSs and UAVs form virtual clusters in a user-centric manner. Within each virtual cluster, non-orthogonal multiple access (NOMA) is adopted for agilely including various maritime IoT devices, which are sparsely distributed over the vast ocean. The nearshore network also shares the spectrum with marine satellites. In such a NOMA-based hybrid satellite-UAV-terrestrial network, interference among different network segments, different clusters, and different users occurs. We thereby formulate a joint power allocation problem to maximize the sum rate of the network. Different from existing studies, we use large-scale channel state information (CSI) only for optimization to reduce system overhead. The large-scale CSI is obtained by using the position information of maritime IoT devices. The problem is non-convex with intractable non-linear constraints. We tackle these difficulties by adopting max-min optimization, the auxiliary function method, and the successive convex approximation technique. An iterative power allocation algorithm is accordingly proposed, which is shown to be effective for coverage enhancement by simulations. This shows the potential of NOMA-based hybrid satellite-UAV-terrestrial networks for maritime on-demand coverage.

Benefiting from the fusion of communication and intelligent technologies, network-enabled robots have become important to support future machine-assisted and unmanned applications. To provide high-quality services for robots in wide areas, hybrid satellite-terrestrial networks are a key technology. Through hybrid networks, computation-intensive and latency-sensitive tasks can be offloaded to mobile edge computing (MEC) servers. However, due to the mobility of mobile robots and unreliable wireless network environments, excessive local computations and frequent service migrations may significantly increase the service delay. To address this issue, this paper aims to minimize the average task completion time for MEC-based offloading initiated by satellite-terrestrial-network-enabled robots. Different from conventional mobility-aware schemes, the proposed scheme makes the offloading decision by jointly considering the mobility control of robots. A joint optimization problem of task offloading and velocity control is formulated. Using Lyapunov optimization, the original optimization is decomposed into a velocity control subproblem and a task offloading subproblem. Then, based on the Markov decision process (MDP), a dual-agent reinforcement learning (RL) algorithm is proposed. The convergence and complexity of the improved RL algorithm are theoretically analyzed, and the simulation results show that the proposed scheme can effectively reduce the offloading delay.

Unsupervised domain adaptation (UDA) becomes more and more popular in tackling real-world problems without ground truths of the target domain. Though tedious annotation work is not required, UDA unavoidably faces two problems: 1) how to narrow the domain discrepancy to boost the transferring performance; 2) how to improve the pseudo annotation producing mechanism for self-supervised learning (SSL). In this paper, we focus on UDA for semantic segmentation tasks. Firstly, we introduce adversarial learning into style gap bridging mechanism to keep the style information from two domains in a similar space. Secondly, to keep the balance of pseudo labels on each category, we propose a category-adaptive threshold mechanism to choose category-wise pseudo labels for SSL. The experiments are conducted using GTA5 as the source domain, Cityscapes as the target domain. The results show that our model outperforms the state-of-the-arts with a noticeable gain on cross-domain adaptation tasks.

Due to the heterogeneity of Internet of Things (IoT) devices and the diversity of IoT communication protocols, it is challenging to defend against malicious traffic from IoT devices. In this paper, a novel malicious traffic detection method is proposed based on the deep learning method. Specifically, a Transformer-based encoder is designed to automatically select key features of IoT traffic for the detection task, which avoids the cumbersome feature screening process that has been widely used in traditional machine learning methods. To address the complexity of the feature space and improve the efficiency of model training, we exploit the correlation between the characteristics of malicious traffic and the device type of IoT bots to further improve the detection accuracy by introducing a device classification auxiliary loss in the training phase. Experimental results show that our method outperforms the state-of-the-art machine learning-based methods in terms of accuracy, precision, recall and f1-score on real IoT traffic traces. In addition, the benefit of device type information on detection efficiency is verified.

The link flooding attack (LFA) has emerged as a new category of distributed denial of service (DDoS) attacks in recent years. Along with the massive deployment of low-cost insecure Internet-of-Things (IoT) devices, the fast proliferation of IoT botnets dramatically increases the risk of LFAs. However, how to efficiently defend against LFAs in IoT still remains as an open problem. To overcome this challenge, we model the interaction between an LFA attacker and the network manager as a two-person Bayesian game in this article to precisely characterize the behaviors of both sides. Then, the rational behaviors of the attacker and the optimal strategies of the defender are unveiled by deriving the Bayesian Nash equilibrium (BNE). Inspired by the obtained BNEs, a cost-effective decision framework is proposed for the defender to make defense decisions. Furthermore, we numerically analyze the effect of all the related factors and present feasible suggestions to deter attack motivations fundamentally. Experimental results demonstrate that the proposed method not only consistently outperforms baseline methods in terms of the defender’s utilities under different attack intensities, but also is robust to the changes in important parameters, including the value of benign traffic and the latency of traffic scrubbing.

The proliferation of Distributed Denial of Service (DDoS) attacks in Internet of Things (IoT) not only threatens the security of digital devices and infrastructure but also severely degrades IoT system performance due to the overly consumed network resources. With the knowledge of identity information of devices and signaling data, Internet service providers (ISPs) can detect and block DDoS traffic by monitoring the upstream IoT packets, and thereby, improve network efficiency. However, inspecting all data packets online for DDoS detection will significantly increase both the network delay and the computational overhead. Therefore, the packet sampling strategy is crucial for the defenders to detect DDoS attacks. To this end, this article formulates a Stackelberg game model to analyze the collaborative IoT packet sampling against DDoS attacks. Through the equilibrium analysis of the DDoS game, we derive the lower bound of packet sampling rate (PSR) that can effectively deter potential attackers. Unlike traditional offline detection, our proposed packet sampling strategy can support both the online detection and proactive prevention of DDoS traffic. As a use case, a multipoint DDoS defense framework is developed to address the IP spoofing in 5G networks based on the proposed packet sampling strategy, which deters DDoS attacks and reduces the packet sampling cost, and thereby, maximizes the IoT utility, compared with existing methods. In typical reflection attacks (in which no more than five packets of response are triggered by a request packet), our proposed scheme not only reduces more than 70% of the sampling rate but also demonstrates superior robustness against boundary condition variation.

Satellite Internet (SI) dramatically expanded the ground-based Internet, and it is also the future direction of 6G. However, due to limited computing power and bandwidth resources, Distributed Denial-of-Service (DDoS) attacks can cause more severe damage to SI, and even paralysis of the entire network. Current DDoS defense mechanisms are built on abundant computing power and bandwidth resources, making applying in the SI scenario challenging. Aiming at protecting SI from DDoS attacks, a blockchain-based distributed collaborative entrance defense (DCED) framework is proposed, in which network traffic characteristics can be recorded and aggregated at the entrances of SI. The proposed framework consists of a distributed detection digesting procedure, a digest virtual aggregation procedure, and an entrance control strategy. The former procedure detects and extracts multidimensional characteristics of DDoS attacks and pushes them onto the blockchain. The latter procedure collects block data and aggregates attack features using the MapReduce algorithm and then compares them with baseline and gives an alert. The strategy completes the filtering and interception of traffic. Experiments use the IXIA platform to generate malicious traffic, and results show that the framework can accurately identify attack traffic within 1500 ms, reaching an area of 0.99 under the receiver operating characteristic curve. The proposed framework is more effective than other similar DDoS methods, protecting the precious SI bandwidth resources.

Orthogonal time frequency space (OTFS) system constitutes an effective structure conceived for efficiently utilizing the channel information, which is capable of achieving a promising transmission performance in high-mobility environment. To extract enough channel diversity, a two-dimensional Fourier transformation combined with a pulse shape is designed at the OTFS transmitter. Consequently, the amplitude of OTFS signals may fluctuate drastically, owing to the combined dependency of the OTFS transformation and the pulse shape. To quantify the amplitude fluctuation, we investigate the peak-to-average power ratio (PAPR) of OTFS signals, for a large amount of data in the delay-Doppler domain. We first reveal that when the number of data points approaches to infinity, based on central limit theorems for dependent variables, the complex-valued OTFS signals weakly converge to a Gaussian distribution. Then, according to the extremal theory of the Chi-squared process for stationary OTFS signals, an accurate expression of the PAPR distribution is derived, depending on the transmit pulse and the number of data points. It is also demonstrated that upon modifying the exponential factor, the analytical PAPR expression is applicable for the non-stationary Gaussian distribution caused by the bandlimited pulse with a large roll-off factor. Simulation results confirm the accuracy of the analytical PAPR probability for practical conditions.

Outside the coverage of terrestrial cellular networks, non-terrestrial infrastructures, e.g., satellites and unmanned aerial vehicles (UAVs), should be utilized, to efficiently cover the remote areas. This requires a cognitive satellite-UAV network, where satellites and UAVs share the spectrum to save cost, and the network resources are orchestrated in an on-demand manner. In this paper, we focus on the physical layer security issue of the cognitive satellite-UAV networks, which is important due to the openness of both satellite links and UAV links. We formulate a joint power and channel allocation problem, using only the slowly-varying large-scale channel state information (CSI), to maximize the sum secrecy rate of UAV users. By resorting to the random matrix theory, the max-min optimization tool, as well as the bipartite graph matching algorithm, we propose a sub-optimal low-complexity solution, the superiority of which is verified by simulation results.

In the blind areas of current fifth generation (5G) networks, e.g. the remote areas, unmanned aerial vehicles (UAVs) can be used to provide on-demand connectivity. To efficiently serve the sparsely distributed users in these areas, non-orthogonal multiple access (NOMA) could be adopted to exploit the user distinguish ability in the power domain. In this paper, we consider a NOMA-based multi-UAV-aided network, where a swarm of coordinated UAVs transmit messages to unevenly distributed users through a virtual multiple-input-multiple-output (MIMO) channel. We formulate a power allocation problem to maximize the minimum user rate to assure fairness in the transmission. Different from existing studies, we use only the large-scale channel state information (CSI) in the transmission design, which characterizes the basic channel feature, and can be obtained using the location information of UAVs/users. By leveraging the random matrix theory and successive convex optimization tools, we propose an iterative algorithm to solve the problem after a series of problem transformation. Simulation results show that the proposed power allocation scheme outperforms existing methods, which shows the potential of multi-UAV-aided NOMA communications for coverage enhancement in remote areas.

With the rapid development of 5G networks, a great amount of Internet of Things (IoT) devices are connected to the Internet. Most of these devices are cost limited and thus are easily compromised by attackers to launch distributed denial of service (DDoS) attacks. The traditional DDoS defense methods at server side can not adapt to this new challenge, thus access-side DDoS detection architecture is urgently needed. In this paper, we propose a deep learning (DL) based IoT device classification method to support fine-grained behavior modeling of malicious traffic and thus enable access-side DDoS detection. Different from traditional studies based on machine learning (ML) which need expertise feature engineering, we propose a time characteristics extraction method based on 1-D convolutional neural network to capture high level time series features automatically for better classification performance. To avoid the feature loss problem, we propose a feature enhancement method based on residual connection module. Experimental results verify the effectiveness of our method, which offers a meaningful gain in terms of both accuracy and macro F1 score over existing approaches.

Structural scene graph based image generation provides a new paradigm for image-oriented semantic communications, whose goal is the semantic level rather than pixel-level reconstruction. The challenges include capturing relationships between objects and producing a reasonable geometric layout for each object accordingly. However, category information alone is not instructive enough for the generation process at the receiver side. Moreover, it is worth effort to extract the spatial dependencies among different objects in an image, therefore determine the object layouts on the whole instead of in an independent manner. In this paper, a deformable geometry framework for scene graph based image generation is proposed, in order to reconstruct images with higher semantic fidelity and visual pleasure. In particular, we introduce shape and appearance information to guide the generation process, from the scope of statistic modeling. Furthermore, we apply a spatial warping network to conduct geometric deformations on the layouts of different objects. Qualitative and quantitative experiments illustrate the superiority of our model compared to the state-of-the-art Sg2im method.

With a significant boost in neuroscience and artificial intelligence, decoding the process of human vision has become a hot topic in the last few decades. Although many existing deep learning models are employed to explore and solve mysteries of human brain activity, the accuracy and reliability of the visual classification task based on electroencephalography (EEG) still have space for promotion. In our research, we design the experiments to collect the subjects’ EEG data when they are watching the different types of images. In this way, an image-EEG dataset corresponding to 80 ImageNet object classes was constructed. Afterward, we proposed a dual-EEGNet for joint feature learning for multi-category visual classification. Especially, one branch EEGNet is used to extract the spatio-temporal embeddings of EEG signals, and the other branch is used to extract the time-frequency embeddings of EEG signals. The experimental results demonstrate that EEG signals can reflect the human brain activity and distinguish the different types of images. Moreover, the proposed model with joint features has a better classification performance in terms of accuracy compared with other methods.

Time-sensitive services (TSSs) have been widely envisioned for future sixth-generation (6G) wireless communication networks. Due to its inherent low-latency advantage, mobile-edge computing (MEC) will be an indispensable enabler for TSSs. The random characteristics of the delay experienced by users are key metrics reflecting the Quality of Service (QoS) of TSSs. Most existing studies on MEC have focused on the average delay. Only a few research efforts have been devoted to other random delay characteristics, such as the delay-bound violation probability and the probability distribution of the delay, by decoupling the transmission and computation processes of MEC. However, if these two processes could not be decoupled, the coupling will bring new challenges to analyze the random delay characteristics. In this article, a MEC system with a limited computation buffer at the edge server is considered. In this system, the transmission process and the computation process form a feedback loop and could not be decoupled. We formulate a discrete-time two-stage tandem queueing system. Then, by using the matrix-geometric method, we obtain the estimation methods for the random delay characteristics, including the probability distribution of the delay, the delay-bound violation probability, the average delay, and the delay standard deviation. The estimation methods are verified by simulations. The random delay characteristics are analyzed by numerical experiments, which unveil the coupling relationship between the transmission process and computation process for MEC. These results will largely facilitate the elaborate allocation of communication and computation resources to improve the QoS of TSSs.

Terrestrial communication networks mainly focus on users in urban areas but have poor coverage performance in harsh environments, such as mountains, deserts, and oceans. Satellites can be exploited to extend the coverage of terrestrial fifth-generation networks. However, satellites are restricted by their high latency and relatively low data rate. Consequently, the integration of terrestrial and satellite components has been widely studied to take advantage of both sides and enable the seamless broadband coverage. Due to the significant differences between satellite communications (SatComs) and terrestrial communications (TerComs) in terms of channel fading, transmission delay, mobility, and coverage performance, the establishment of an efficient hybrid satellite-terrestrial network (HSTN) still faces many challenges. In general, it is difficult to decompose an HSTN into a sum of separate satellite and terrestrial links due to the complicated coupling relationships therein. To uncover the complete picture of HSTNs, we regard the HSTN as a combination of basic cooperative models that contain the main traits of satellite-terrestrial integration but are much simpler and thus more tractable than the large-scale heterogeneous HSTNs. In particular, we present three basic cooperative models, i.e., model X, model L, and model V, and provide a survey of the state-of-the-art technologies for each of them. We discuss future research directions toward establishing a cell-free, hierarchical, decoupled HSTN. We also outline open issues to envision an agile, smart, and secure HSTN for the sixth-generation ubiquitous Internet of Things.

With the rapid development of marine activities, there has been an increasing number of Internet-of-Things (IoT) devices on the ocean. This leads to a growing demand for high-speed and ultrareliable maritime communications. It has been reported that a large performance loss is often inevitable if the existing fourth-generation (4G), fifth-generation (5G), or satellite communication technologies are used directly on the ocean. Hence, conventional theories and methods need to be tailored to this maritime scenario to match its unique characteristics, such as dynamic electromagnetic propagation environments, geometrically limited available base station (BS) sites and rigorous service demands from mission-critical applications. Toward this end, we provide a survey on the demand for maritime communications enabled by state-of-the-art hybrid satellite-terrestrial maritime communication networks (MCNs). We categorize the enabling technologies into three types based on their aims: 1) enhancing transmission efficiency; 2) extending network coverage; and 3) provisioning maritime-specific services. Future developments and open issues are also discussed. Based on this discussion, we envision the use of external auxiliary information, such as sea state and atmosphere conditions, to build up an environment-aware, service-driven, and integrated satellite-air-ground MCN.

In fifth generation (5G) and beyond Internet of Things (IoT), it becomes increasingly important to serve a massive number of IoT devices outside the coverage of terrestrial cellular networks. Due to their own limitations, unmanned aerial vehicles (UAVs) and satellites need to coordinate with each other in the coverage holes of 5G, leading to a cognitive satellite-UAV network (CSUN). In this paper, we investigate multi-domain resource allocation for CSUNs consisting of a satellite and a swarm of UAVs, so as to improve the efficiency of massive access in wide areas. Particularly, the cell-free on-demand coverage is established to overcome the cost-ineffectiveness of conventional cellular architecture. Opportunistic spectrum sharing is also implemented to cope with the spectrum scarcity problem. To this end, a process-oriented optimization framework is proposed for jointly allocating subchannels, transmit power and hovering times, which considers the whole flight process of UAVs and uses only the slowly-varying large-scale channel state information (CSI). Under the on-board energy constraints of UAVs and interference temperature constraints from UAV swarm to satellite users, we present iterative multi-domain resource allocation algorithms to improve network efficiency with guaranteed user fairness. Simulation results demonstrate the superiority of the proposed algorithms. Moreover, the adaptive cell-free coverage pattern is observed, which implies a promising way to efficiently serve wide-area IoT devices in the upcoming sixth generation (6G) era.

Reconfigurable intelligent surfaces (RISs), consisting of many low-cost elements that reflect the incident waves by an adjustable phase shift, have attracted sudden attention for their potential of reconfiguring the signal propagation environment and enhancing the performance of wireless networks. The passive nature of RISs is indeed beneficial, but the lack of radio frequency (RF) chains at the RIS has made channel estimation extremely challenging. We face this challenge by proposing a joint channel estimation and transmit precoding framework for RIS-aided multiple-input multiple-output (MIMO) systems. Specifically, the effective cascaded channel of the reflected transmitter-RIS-receiver link is decomposed into multiple subchannels, each of which corresponds to a single RIS element. Then our joint RIS-transmitter precoding model is formulated for the individual subchannels of each reflecting element. Finally, we develop a two-stage precoding design for successively determining the required phase shifts of each reflecting element of the RIS and the digital baseband precoder of the transmitter, only relying on the channel state information (CSI) of the subchannels. The performance of the proposed subchannel estimation and joint precoding method is evaluated by extensive simulations. Our numerical results show that the proposed designs provide an attractive solution to RIS-aided MIMO systems.

Distributed Reflection Denial of Service (DRDoS) attack has become one of the most serious threats to Internet security. With the ongoing development of 5G, a massive number of insecure Internet of Things (IoT) devices are connected to the Internet, which brings great challenges to defend against DRDoS attacks. To overcome these challenges, we extend the User Plane Function (UPF) of 5G core network, and propose a new framework accordingly for source IP address validation, so as to suppress the source IP address spoofing behaviors of DRDoS attackers. Under this framework, the packet inspection rate (PIR), i.e., the inspection probability of each packet, is crucial to simplify the validation complexity. To unveil the optimal PIR, we establish a two-player game which models the IP address spoofing and detection behaviors. Analysis on the formulated game implies a lower bound of sufficient PIR, which may be used to set PIR in practice. Simulation results show that the proposed method can efficiently deter IP spoofing behaviors. Thereby the derived PIR could achieve low-cost and effective defense of DRDoS.

In extreme communication environments, a highly dynamic channel (HDC) often arises with quite challenging fast time-varying and nonstationary characteristics. Different from existing studies, in this work, we investigate the most intractable HDC case when the coherence time of the channel is smaller than the symbol period. We propose a deep neural network (DNN)-based symbol detector using the long short-term memory (LSTM) neural network. Particularly, the sampling sequence of the received signal per symbol is used as the input data of each LSTM unit, which can take advantage of all received information and thus achieve better performance. Furthermore, a preprocessing unit using the basis expansion model (BEM) is designed to dramatically reduce the number of parameters while training the neural network, and the BEM-DNN-based detector achieves almost the same performance as the DNNbased detector. Finally, simulation results are achieved using the highly dynamic plasma sheath channel (HDPSC) data measured from realistic shock tube experiments. The results show that the proposed DNN-based method outperforms conventional methods and requires no prior channel estimation or knowledge of channel models.

The link flooding attack (LFA) arises as a new class of Distributed Denial of Service (DDoS) attacks in recent years. By aggregating low-rate protocol-conforming traffic to congest selected links, LFAs can degrade the connectivity of target servers indirectly. Due to the fast proliferation of insecure Internet of Things (IoT) devices, the deployment of botnets is getting easier, which dramatically increases the risk of LFAs. Since the attacking traffic may not reach the victims directly and seems to be legitimate, LFAs are extremely difficult to detect and defend using traditional methods. In this work, we model the interaction between the LFA attacker and the defender as an extensive form game with incomplete information. By using action space compression and the divide and conquer method, we analyze the Nash equilibrium of the subgame on each link, which reveals the rational behaviors of attackers and the optimal strategies of defenders. Furthermore, we concretely expound how to adopt local optimal strategies in the Internet-wide scenario. Experimental results show the effectiveness and robustness of our proposed decision-making method in explicit LFA defending scenarios.

Reconfigurable intelligent surfaces (RISs), which can deliberately adjust the phase of incident waves, have shown enormous potentials to reconFigure the signal propagation for performance enhancement. In this paper, we investigate the RIS-aided offshore system to provide a cost-effective coverage of high-speed data service. The shipborne RIS is placed offshore to improve the signal quality at the vessels, and the coastal base station is equipped with low-cost reconfigurable reflect-arrays (RRAs), instead of the conventional costly fully digital antenna arrays (FDAAs), to reduce the hardware cost. In order to meet the rate requirements of diversified maritime activities, the effective sum rate (ESR) is studied by jointly optimizing the beamforming scheme and the service time allocated to each vessel. The optimal allocation scheme is derived, and an efficient fixed-point based alternating ascent method is developed to obtain a suboptimal solution to the non-convex beamforming problem. Numerical results show that the ESR is considerably improved with the aid of the RIS, and the proposed scheme using the hardware-efficient RRAs has only a slight performance loss, compared to its FDAA-based counterpart.

Due to the agile maneuverability, unmanned aerial vehicles (UAVs) have shown great promise for on-demand communications. In practice, UAV-aided aerial base stations are not separate. Instead, they rely on existing satellites/terrestrial systems for spectrum sharing and efficient backhaul. In this case, how to coordinate satellites, UAVs and terrestrial systems is still an open issue. In this paper, we deploy UAVs for coverage enhancement of a hybrid satellite-terrestrial maritime communication network. Using a typical composite channel model including both large-scale and small-scale fading, the UAV trajectory and in-flight transmit power are jointly optimized, subject to constraints on UAV kinematics, tolerable interference, backhaul, and the total energy of the UAV for communications. Different from existing studies, only the location-dependent large-scale channel state information (CSI) is assumed available, because it is difficult to obtain the small-scale CSI before takeoff in practice and the ship positions can be obtained via the dedicated maritime Automatic Identification System. The optimization problem is non-convex. We solve it by using problem decomposition, successive convex optimization and bisection searching tools. Simulation results demonstrate that the UAV fits well with existing satellite and terrestrial systems, using the proposed optimization framework.

Unmanned aerial vehicle (UAV) swarm-enabled aerial network has emerged as an effective solution to ondemand communications, especially in unexpected scenarios. Due to the broadcast nature of the air-to-ground link, UAV swarm-enabled wireless communications are inherently prone to eavesdropping. The paper investigates power allocation for UAV swarm-enabled secure networks. To depict air-to-ground link, a composite channel consisting of small-scale and large-scale fading is taken into account. Because of the difficulty in acquiring the time-varying small-scale fading, we use the large-scale channel state information (CSI). An optimization framework in a whole- trajectory-oriented manner is proposed to maximize secrecy throughput with the constraints on the transmission power and the transmission durations as well as the overall transmission energy per UAV over a given flight period. The formulated problem is not convex. To deal with that, we first derive a closed form of secrecy throughput in the form of high-order fixed-point equations. Then, we propose an iterative algorithm with successive convex approximation technique by alternately optimizing the variables. Numerical results validate the effectiveness of the proposed scheme and show that our proposed scheme can achieve a good secrecy performance.

The multibeam satellite system (MBSS) has great potential for mobile communications in 5G era due to its superiority in terms of extensive coverage, large capacity and real-time service. In order to integrate the resource allocation in multiple dimensions and maximize the system capacity of the MBSS, the scenario of a sparse network with dense users is selected to investigate the resource allocation method in time dimension, frequency dimension, space dimension and power dimension. We first propose a multilevel clustering algorithm and a cross-cluster grouping algorithm to realize the beam scheduling, by which the interference is reduced in time dimension and space dimension. Based on the beam scheduling scheme, we further explore the relationship between the system capacity and the resource allocation in frequency dimension and power dimension, where a joint power allocation and subchannel selection algorithm is proposed to optimize the spectral efficiency. Our simulation results show that the proposed multiple-dimension resource allocation method is superior to the existing methods in system capacity and convergence, which is not only applicable for the resource allocation in the MBSS but also provides an efficient approach to solve the coupling resource allocation problem.

The unmanned aerial vehicle (UAV) is a promising enabler of the Internet of Things (IoT) vision, due to its agile maneuverability. In this paper, we explore the potential gain of UAV-aided data collection in a generalized IoT scenario. Particularly, a composite channel model, including both large-scale and small-scale fading is used to depict typical propagation environments. Moreover, rigorous energy constraints are considered to characterize IoT devices as practically as possible. A multiantenna UAV is employed, which can communicate with a cluster of single-antenna IoT devices to form a virtual MIMO link. We formulate a whole-trajectory-oriented optimization problem, where the transmission duration and the transmit power of all devices are jointly designed to maximize the data collection efficiency for the whole flight. Different from previous studies, only the slowly varying large-scale channel state information is assumed available, to coincide with the fact that practically it is quite difficult to predictively acquire the random small-scale channel fading prior to the UAV flight. We propose an iterative scheme to overcome the nonconvexity of the formulated problem. The presented scheme can provide a significant performance gain over traditional schemes and converges quickly.

In this paper, we propose an architecture of cloud based integrated terrestrial-satellite networks, in which satellite and terrestrial networks that belong to the same operator cooperatively provide seamless coverage for mobile users. Meanwhile, a resource pool at the cloud acts as the integrated resource management and control center of the entire network. Then, based on the delay constraint of users, we formulate the resource allocation problem for the operator to minimize the energy consumption. By decomposing the optimization problem into two subproblems and utilizing the theory of multidimensional knapsack problem, we eventually obtain the optimal resource allocation strategies for the operator. Furthermore, numerical results are provided to evaluate the performance of the proposed strategies.

Memristor with tunable non-volatile resistance offers in-memory computing capability that avoids the von-Neumann bottleneck. However, large-scale experimental demonstration to this end is yet to be implemented due to the immaturity of the device and integration technologies. Here in this paper we report our recent process in analog computing using analog-voltage-amplitude-vector input and analog-memristor-conductance matrix, with applications in signal and image processing. The vector matrix multiplication is processed in the memristor crossbars in one step, with 5-8 bit precision depending on the array size. The demonstration is made possible by high memristor yield (99.8%), stable multilevel memresistance states, linear current-voltage (IV) relation in the operation range, and low wire resistance between the cells.

Unmanned aerial vehicle (UAV) communication provides a promising solution to emergency recovery and coverage extension. For Internet of Things (IoT) communications system, utilizing UAV as an on-demand gateway is an efficient technique to enable the communication between IoT devices over a long distance. In this paper, we investigate a mobile UAV-aided Internet of Things (IoT) communications system, where one UAV actes as a dynamic aerial base station to serve all the IoT devices. We aim to maximize the sum rate of the mobile UAV by jointly optimizing IoT device-UAV scheduling, the uplink transmission power of the IoT devices and the UAV altitude. This optimization is a mixed-integer non-convex problem, and an efficient iterative algorithm is proposed by means of the block coordinate descent technique. Finally, simulation results are presented to demonstrate that our proposed scheme significantly outperforms the existing one.

In a device-to-device (D2D) communications underlaying cellular network, any user is a potential eavesdropper for the transmissions of others that occupy the same spectrum. The physical-layer security mechanism of theoretical secure capacity, which maximizes the rate of reliable communication from the source user to the legitimate receiver and ensure unauthorized users learn as little as information as possible, is typically employed to guarantee secure communications. As hand-held devices are carried by human beings, we may leverage their social trust to decrease the number of potential eavesdroppers. Aiming to establish a new paradigm for solving the challenging problem of security and efficiency tradeoff, we propose a social trust-aware D2D communication architecture that exploits the social-domain trust for securing the physical-domain communication. In order to understand the impact of social trust on the security of transmissions, we analyze the system ergodic rate of social trust aided communications via stochastic geometry, and our result based on a real data set shows that the proposed social trust aided D2D communication increases the system secrecy rate by about 63% compared with the scheme without considering social trust relation. Furthermore, in order to provide implementation mechanism, we utilize matching theory to implement efficient resource allocation among multiple users. Numerical results show that our proposed mechanism increases the system secrecy rate by 28% with fast convergence over the social oblivious approach.

In this paper, we investigate the downlink cooperative multigroup multicast transmission in the integrated terrestrial-satellite network, in which base stations (BSs) and the satellite provide the multicast service for ground users in a cooperative manner while reusing the entire bandwidth. For both terrestrial BSs and the satellite, multiantennas are equipped and beamforming techniques are utilized for improving the system performance. Based on the architecture, we formulate a weighted max-min fair (MMF) beamforming design problem to jointly optimize the beamforming vectors of BSs and the satellite, which is solved based on the relation between the quality of service problem and the MMF problem. When it comes to the large scale case, where large numbers of BSs are distributed within the coverage of the satellite, we propose a time division cooperative multigroup multicast scheme consisting of two phases for the BSs and the satellite. Then, an iterative algorithm is proposed to solve the weighted MMF problem in the time division case. Finally, numerical results are provided to evaluate the cooperative multicast schemes as well as the proposed algorithms.

Spin-transfer torque random access memory (STT-RAM) is a promising technology for future nonvolatile caches and memories. To increase the storage density, multilevel cell (MLC) technique was recently introduced to STT-RAM designs at the cost of degraded access speed, reliability, and energy efficiency. Existing MLC STT-RAM cache architectures primarily focus on the performance and energy optimizations but ignore the crucial demand for reliability. In this paper, we propose “TriZone”-a holistic design scheme for MLC STT-RAM cache to simultaneously meet the requirements of performance, energy, and reliability. Three cache block configurations, namely hard, soft, and mixed, are constructed with the hard-bit, soft-bit, and both hard-bit and soft-bit of MLC STT-RAM, respectively. By observing the difference of these cache blocks, a nonuniform strength ECC (NUS-ECC) is developed to guarantee the operational reliability of a cache block with a variable decoding delay adapting to the needs of error correction (e.g., the number of the erroneous bits). The whole MLC STT-RAM cache is then partitioned into three regions, each of which is composed of different cache blocks. In order to achieve the best tradeoff among performance, energy, and reliability, we then introduce the dynamic cache partitioning to determine the partition of this tri-way MLC STT-RAM cache according to the runtime characteristic of various applications. Experiment results show that compared with conventional performance-driven MLC STT-RAM cache design with pessimistic ECC, TriZone can improve the system performance and energy by averagely 11.7% (10.0%) and 13.3% (15.7%), respectively, for single-threaded (multiprogram) applications. The additional area overhead associated with NUS-ECC is limited by ~ 3%.

The introduction of wireless transmissions into the data center has shown to be promising in improving cost effectiveness of data center networks (DCNs). For high transmission flexibility and performance, a fundamental challenge is to increase the wireless availability and enable fully hybrid and seamless transmissions over both wired and wireless DCN components. Rather than limiting the number of wireless radios by the size of top-of-rack switches, we propose a novel DCN architecture, Diamond, which nests the wired DCN with radios equipped on all servers. To harvest the gain allowed by the rich reconfigurable wireless resources, we propose the low-cost deployment of scalable 3-D ring reflection spaces (RRSs) which are interconnected with streamlined wired herringbone to enable large number of concurrent wireless transmissions through high-performance multi-reflection of radio signals over metal. To increase the number of concurrent wireless transmissions within each RRS, we propose a precise reflection method to reduce the wireless interference. We build a 60-GHz-based testbed to demonstrate the function and transmission ability of our proposed architecture. We further perform extensive simulations to show the significant performance gain of diamond, in supporting up to five times higher server-to-server capacity, enabling network-wide load balancing, and ensuring high fault tolerance.

The plasma sheath channel has a serious impact on the propagation of electromagnetic waves, resulting in the radio blackout problem in aerospace communications. In existing studies, the plasma sheath channel characteristics are analyzed based on computer simulations, lack of real-world experimental verification. Thereby, these studies failed to fully demonstrate the high-dynamics of the plasma sheath channel. In this paper, an experimental communication system based on the shock tube is proposed to investigate the plasma sheath channel. Then, the characteristics of the plasma sheath channel are analyzed based on the experimental results. In particular, the high-dynamic and fast time-varying channel characteristics are verified by analyzing the signal amplitude, signal phase shift and the coherence time. Finally, we show an example of using the presented channel characteristics. A non-stationary signal segmentation method is proposed based on the reversible jump Markov chain Monte Carlo algorithm, which is applicable to the plasma sheath channel signal segmentation.

The contradiction between ever increasing satellite communication traffic and limited satellite transponder resources motivates a more dynamic allocation and more effective utilization of satellite transponders' resources. In this paper, the link capacity for a smart satellite transponder is maximized with limited available power and spectrum resource at satellite transponder. Specifically, given that the satellite transponder broadcasts the signals from the gateway station to the multiple satellite terminals with minimum transmission rate requirement, the satellite transponder needs to provide as large link capacity as possible to gateway station for the amount data transmission of special demands. The problem is formulated with aim of maximizing link capacity, and subject to minimum transmission rate requirement of link to satellite terminals and available resource allocation. The finely-matched dynamic power and spectrum allocation scheme is proposed to achieve maximization of both target link capacity and transponder resource utilization. Simulations results demonstrate that the proposed scheme outperforms tradition schemes and the superiority is even more remarkable in multi-constrained situations.

Increasing marine activities taking place within the exclusive economic zone (EEZ) have made broadband maritime communications very attractive in recent years. In this paper, a coordinated satellite and terrestrial architecture (II-CST) is presented to implement real-time and broadband Internet access at sea. Further, since the mobility of shipborne base stations (S-BSs) may cause complex inter-cell interference (ICI), an S-BS sleeping control scheme is proposed to adapt the system resource allocation corresponding with the user load. When the proportion of blocked users is beyond threshold, the sleeping control process is triggered to reduce ICI and recover broadband access. Then we use the sailing position of accessed users as a constraint to adjust the downlink power allocation of S-BSs. Simulation results with real ship data of China's Yellow Sea show that under the II-CST, our adaptive S-BS sleeping control can contribute to achievable downlink data rate, mobility robustness, and power efficiency of the dynamic broadband maritime communication system.

In data relay satellite (DRS) systems, the performance of tasks scheduling is influenced by the variation of task and resources, which degrades the processing capacity of relay satellites. Considering this problem, we investigate the dynamic scheduling in the application of DRS. To achieve the efficient resource utilization and reliable data transfer, the strategies of task preemptive switching and decomposition are designed. Based on the initial scheme, we construct a dynamic scheduling model with multiple objectives, including maximizing the total weight of scheduled tasks, minimizing the change of scheduling scheme and minimizing the number of decomposed subtasks. Meanwhile, a preemptive dynamic scheduling algorithm (PDSA) is designed to solve the proposed model. Explicitly, our simulation results show that PDSA is superior to the whole rescheduling algorithm (WRA) in quantities of completed tasks, rescheduling rate of scheme and processing time, which can efficiently improve the performance of dynamic scheduling in DRS systems.

Increasing marine activities taking place within the exclusive economic zone (EEZ) have made broadband maritime communications very attractive in recent years. In this paper, a coordinated satellite and terrestrial (II-CST) architecture is presented to enable real-time and broadband Internet access within the EEZ. Further, a voyage-based resource allocation scheme is proposed to dynamically adapt the system resource consumption corresponding with the user load. We make switching plans for shipborne base stations (S-BSs) considering the proportion of blocked users and changes in user distribution to recover broadband access and reduce inter-cell interference at sea, and take the sailing position of accessed users as a constraint to cooperatively adjust the downlink power of S-BSs. Simulation results under real ship data of China's Yellow Sea show that under the II-CST architecture, our resource allocation scheme can effectively improve the signal to interference noise ratio (SINR) of users, reduce the number of handover-related link failures, and reduce the power consumption of S-BSs.

The performance of a frequency division duplexing (FDD) massive multiple input multiple output (MIMO) system is traditionally limited by the large amount of overhead for downlink channel training and uplink channel state information (CSI) feedback. In this paper, we propose an environment-aware scheme to exploit massive MIMO gains in the FDD mode. Under a quasi-static scattering geometry and slow user mobility, the propagation environment can be known at a low cost. Given a priori environment information, the angular domain channel statistics can be obtained accordingly. In the proposed scheme, the angular domain is partitioned into several angular bins and the same number of predefined precoding vectors are generated accordingly. Based on the environment-specific angular domain information, the system topology is modeled as a bipartite graph. An efficient user scheduling algorithm is proposed, which is equivalent to finding a match of the bipartite graph, and a remarkable multiplexing gain is achieved by removing the overlapped angular bins. After user scheduling, each selected user is allocated to one predefined precoding vector. The numerical results have confirmed the validity of the proposed scheme.

In distributed multiple‐input multiple‐output (D‐MIMO) systems, the improvements of spectral and energy efficiencies rely heavily on the accuracy of the channel state information (CSI). In order to enhance the accuracy of CSI acquisition, the problem of pilot design in a D‐MIMO system is addressed in this study. In particular, the authors focus on the optimisation of pilot power adaptation in a single‐cell D‐MIMO system with multiple users served in orthogonal resources. Under the concept of tomographic channel estimation, the problem of pilot power adaptation aiming at maximising the lower bound of the sum capacity is formulated. As the computational complexity of solving the problem is relatively high due to the mutual coupling constraints, the dual decomposition technique is introduced to decouple the constraints and reduce the complexity via parallel computation. An effective pilot power adaptation scheme is further proposed by using the projected subgradient method. The superiority and effectiveness of the proposed scheme are illustrated by the simulation results.

Millimeter-wave (mmWave) communication is envisioned to provide orders of magnitude capacity improvement. However, it is challenging to realize a sufficient link margin due to high path loss and blockages. To address this difficulty, in this paper, we explore the potential gain of ultra-densification for enhancing mmWave communications from a network-level perspective. By deploying the mmWave base stations (BSs) in an extremely dense and amorphous fashion, the access distance is reduced and the choice of serving BSs is enriched for each user, which are intuitively effective for mitigating the propagation loss and blockages. Nevertheless, co-channel interference under this model will become a performance-limiting factor. To solve this problem, we propose a large-scale channel state information (CSI)-based interference coordination approach. Note that the large-scale CSI is highly location-dependent, and can be obtained with a quite low cost. Thus, the scalability of the proposed coordination framework can be guaranteed. Particularly, using only the large-scale CSI of interference links, a coordinated frequency resource block allocation problem is formulated for maximizing the minimum achievable rate of the users, which is uncovered to be an NP-hard integer programming problem. To circumvent this difficulty, a greedy scheme with polynomial-time complexity is proposed by adopting the bisection method and linear integer programming tools. Simulation results demonstrate that the proposed coordination scheme based on large-scale CSI only can still offer substantial gains over the existing methods. Moreover, although the proposed scheme is only guaranteed to converge to a local optimum, it performs well in terms of both user fairness and system efficiency.

The recent concept of beamspace multiple input multiple output (MIMO) can significantly reduce the number of required radio frequency (RF) chains in millimeter-wave (mmWave) massive MIMO systems without obvious performance loss. However, the fundamental limit of existing beamspace MIMO is that the number of supported users cannot be larger than the number of RF chains at the same time-frequency resources. To break this fundamental limit, in this paper, we propose a new spectrum and energy-efficient mmWave transmission scheme that integrates the concept of non-orthogonal multiple access (NOMA) with beamspace MIMO, i.e., beamspace MIMO-NOMA. By using NOMA in beamspace MIMO systems, the number of supported users can be larger than the number of RF chains at the same time-frequency resources. In particular, the achievable sum rate of the proposed beamspace MIMO-NOMA in a typical mmWave channel model is analyzed, which shows an obvious performance gain compared with the existing beamspace MIMO. Then, a precoding scheme based on the principle of zero forcing is designed to reduce the inter-beam interferences in the beamspace MIMO-NOMA system. Furthermore, to maximize the achievable sum rate, a dynamic power allocation is proposed by solving the joint power optimization problem, which not only includes the intra-beam power optimization, but also considers the inter-beam power optimization. Finally, an iterative optimization algorithm with low complexity is developed to realize the dynamic power allocation. Simulation results show that the proposed beamspace MIMO-NOMA can achieve higher spectrum and energy efficiency compared with the existing beamspace MIMO.

In this paper, we investigate the downlink transmission of a non-orthogonal multiple access (NOMA)-based integrated terrestrial-satellite network, in which the NOMA-based terrestrial networks and the satellite cooperatively provide coverage for ground users while reusing the entire bandwidth. For both terrestrial networks and the satellite network, multi-antennas are equipped and beamforming techniques are utilized to serve multiple users simultaneously. A channel quality-based scheme is proposed to select users for the satellite, and we then formulate the terrestrial user pairing as a max-min problem to maximize the minimum channel correlation between users in one NOMA group. Since the terrestrial networks and the satellite network will cause interference to each other, we first investigate the capacity performance of the terrestrial networks and the satellite networks separately, which can be decomposed into the designing of beamforming vectors and the power allocation schemes. Then, a joint iteration algorithm is proposed to maximize the total system capacity, where we introduce the interference temperature limit for the satellite since the satellite can cause interference to all base station users. Finally, numerical results are provided to evaluate the user paring scheme as well as the total system performance, in comparison with some other proposed algorithms and existing algorithms.

Due to spectrum sharing, device-to-device (D2D) communications underlaying cellular networks enhance system capacity significantly that benefits services of local area. On the other hand, network coding enables highly efficient cooperation, which increases the system capacity through code-and-forward mechanism. It is a challenging problem that how to allocate resource in the network coding aided cooperative D2D communications. In this paper, we first design a network coding aided cooperative diversity scheme for D2D communication, and derive the system transmission rate with the consideration of interference. Then, we formulate the problem of joint spectrum resource allocation and relay selection as an overlapping coalition formation game, where one relay is able to serve multiple coalitions to increase the system capacity. For each coalition, maximum bipartite graph matching model is established to select the optimal relay to achieve the maximum transmission rate. Finally, to solve the formulated game problem, we propose a distributed algorithm based on switch operations with low computation complexity. Extensive numerical results demonstrate that our solution increases the system transmission rate by about 30-40 percent without bringing extra computation complexity, compared with other state-of-the-art schemes.

Vector-matrix multiplication dominates the computation time and energy for many workloads, particularly neural network algorithms and linear transforms (e.g, the Discrete Fourier Transform). Utilizing the natural current accumulation feature of memristor crossbar, we developed the Dot-Product Engine (DPE) as a high density, high power efficiency accelerator for approximate matrix-vector multiplication. We firstly invented a conversion algorithm to map arbitrary matrix values appropriately to memristor conductances in a realistic crossbar array, accounting for device physics and circuit issues to reduce computational errors. The accurate device resistance programming in large arrays is enabled by close-loop pulse tuning and access transistors. To validate our approach, we simulated and benchmarked one of the state-of-the-art neural networks for pattern recognition on the DPEs. The result shows no accuracy degradation compared to software approach (99 % pattern recognition accuracy for MNIST data set) with only 4 Bit DAC/ADC requirement, while the DPE can achieve a speed-efficiency product of 1,000× to 10,000× compared to a custom digital ASIC.

This paper studies distributionally robust chance-constrained minimum variance beamforming. In contrast to deterministic modeling of the steering vector, our approach models the uncertainty statistically via distributions. We select the weights that minimize the combined output power subject to the distributionally robust chance constraint that for all distributions in the uncertainty set, the gain should exceed unity with high probability. Our discussion begins with the simplest case where the distributional set contains only Gaussian distribution; then we derive the robust weights for three distributional sets, namely, the set of (central symmetric) distributions with known mean and covariance, and a distributional model where the mean is known, the components are independent and belong to some known bounded intervals. It can be seen that these four robust beamformers provide statistical interpretation for the deterministic counterpart. Finally, we demonstrate the performance of these robust beamformers via several numerical examples.

Existing channel estimators for amplify-and-forward relaying often transmit pilots to the destination node by using the relay node's own energy. This extra energy consumption discourages the relay node from taking part in relaying. We propose two new estimators for amplify-and-forward relaying channels. In these estimators, the relay node harvests energy from the pilots sent by the source node first and then uses the harvested energy to forward the pilots from the source node as well as transmit its own pilots to the destination node. Both time-switching and power-splitting harvesting strategies are considered. The mean squared error is examined. Numerical results show that these new estimators have very good performances. They also show that optimum choices of certain system parameters exist.

In this paper, we propose a novel gaze estimation method to evaluate the attention span of users upon on-screen content via a single webcam. Our method is based on supervised descent method for eye region of interest (ROI) extraction. Then, boost Gaussian Bayesian regressors are applied to learn a robust mapping from the input eye ROI to gaze coordinates. To get enough training samples, we implant our scheme as a plug-in into web browsers for data collection from users without bothering. To improve accuracy, we also introduce mouse click to help train the regressors. Experiment results show that our method outperforms the existing method and can provide gaze estimation data for user behaviour analysis in real-time implementation.

As technology moves into nano-realm, large area of the chip is especially vulnerable to single event upset (SEU) in space applications. In this paper, low cost fault-tolerant schemes are presented for the key modules of Low-Density Parity-Check (LDPC) code decoder to save logic resources. For counters, a fault-tolerant scheme based on m-sequence and Hamming coding is proposed, whereby the soft errors generated by SEUs can be located and corrected by a simple Hamming decoder. For RAM contents, we first propose a layered pipelined architecture absorbing LLR RAM into V2C RAM to reduce memory bits, which will lower the impact of SEUs. Then, a RAM hardening scheme is proposed, which only requires to detect soft errors by parity check, while the error correction is accomplished by decoder's own iterative decoding capability that has not been exploited sufficiently. Simulation results show that the proposed fault-tolerant counter could totally avoid SEUs and saves 42% of cell area compared with TMR method and the layered pipelined architecture saves 42% and 12% of memory bits compared with [4] and [15]. In addition, the hardened RAM cells will not cause extra bit errors when a soft error happens under the environments of high signal-to-noise ratio (SNR). The cost is only one parity bit for each RAM content, which is much less than conventional hardening schemes.

Most recently, there has been a growing interest in the study of dictionary learning based (DL-based) image compression, which has potential in relieving the bandwith-hungry bottleneck of visual communication. All existing DL-based image compression approaches mainly focus on the effective representation of images, thus losing sight of two basic elements of image compression, i.e., quantization and entropy coding. For this reason, this paper proposes a quantization and entropy coding scheme for DL-based image compression. In our scheme, the proposed Partition-Interval K-means (PIK) quantizer adaptively maps continuous coefficients to discrete values. The arithmetic coding, combined with differential coding technique, is applied to encode the indices of nonzero coefficients as well as the labels of quantization values. In our experiments, the proposed scheme is verified to be more effective than other quantization and entropy coding schemes for DL-based image compression.

With the ever-increasing energy consumption in transmissions of explosive growing mobile data, energy-efficient solutions are needed to be integrated into the future mobile networks. The upcoming 5G networks support device-to-device (D2D) communication underlaying the cellular networks, which enables proximity cellular users to communicate directly with high data rate and low transmit power. In this paper, targeting the energy-aware D2D communications underlaying cellular system, we investigate the fundamental problems of what is the potential gains of D2D communications for energy saving, which are the fundamental reasons to decrease the energy consumption, and how about the tradeoffs between energy consumption and other network factors of available bandwidth, buffer size and service delay in large scale D2D communication networks. To answer the above challenging problems, we utilize a dynamic graph approach to model the system with human mobility for a realistic D2D communication scenario. Specifically, by formulating a mixed integer linear programming problem that minimizes the energy consumption for data transmission from the cellular base stations to the receivers through any possible ways of transmissions, we obtain the theoretical performance lower bound of system energy consumption, which shows that cellular D2D communications decrease the energy consumption about 65% averagely under the realistic scenario. Furthermore, the obtained fundamental tradeoffs reveal that energy consumption for large bandwidth can be kept at a low level with increase of the buffer size and service delay.

The massive multiple input multiple output (MIMO) technique is a promising candidate to enormously increase the capacity of wireless networks. In a massive MIMO system, coordinated signal processing among different antennas is crucial to cope with the inevitable co-channel interference. However, it is normally difficult to perform perfect coordination in practical applications, due to the challenging requirement of global channel state information at the transmitter (CSIT). To solve this problem, this paper considers a massively distributed MIMO model, and presents dynamic-cell (DC)-based macro coordination, which requires only the instantaneous intra-DC CSIT and the slowly-varying large-scale inter-DC CSIT. Particularly, we first divide the system into a number of coupled user-centric DCs. Perfect coordination in the form of maximum ratio transmission (MRT) is adopted locally within each DCs based on instantaneous intra-DC CSIT. Then, we propose an inter-DC coordination approach termed as enhanced MRT to mitigate the inter-DC interference. The coordination is designed on the basis of large-scale inter-DC CSIT, which thus is referred to as macro coordination. Simulation results demonstrate that the proposed DC-based macro coordination can achieve a satisfactory performance gain in terms of system sum rate, while requiring much less CSIT than traditional schemes.

Device-to-Device (D2D) communication underlaying cellular networks enhances the system capacity by efficient spectrum utilization, which significantly benefits local area services. On the other hand, physical layer network coding (PNC) can achieve more efficient two-way transmission. Thus, PNC aided two- way D2D communications have the potential to further increase the system capacity, where joint resource allocation and relay selection is a challenging problem. In this paper, we first present the PNC aided two-way D2D communication scheme over two or three time slots, and derive the corresponding achievable rate under the interferences between the relays and D2D pairs that are sharing the spectrum resource. Then, we formulate the problem of joint resource spectrum and relay selection as an optimization problem, which is NP-hard. Finally, to solve the formulated problem, we propose a distributed algorithm based on coalition game, which deceases the computation complexity significantly. Through extensive simulations, we demonstrate the effective of system performance of different transmission schemes in the two-way D2D communication underlaying cellular networks.

This paper investigates distributionally robust minimum variance beamforming under first-order moment uncertainty. In contrast to deterministic modeling of the array response, our approach employs a distributional set to describe the uncertainty. The distributional set we introduce consists of two constraints: the probability measure constraint and a first-order moment constraint. The weights are selected to minimize the combined output power, subject to the modified distortionless response constraint that the expected real part of the array gain exceeds unity for all distributions in the uncertainty set. We begin our discussion by revealing the intrinsic connection between the distributionally robust minimum variance beamformers (DRMVB) and the robust minimum variance beamformer (RMVB). Then for the sample space described by a union of ellipsoids, the DRMVB is reformulated as the optimal solution of a semidefinite program (SDP). Finally, we demonstrate the performance of the DRMVB via several numerical examples.

When non-orthogonal pilots are used in multi-cell systems, channel estimation would be corrupted by inter-cell interference. To tackle this problem, the design of pilot sequences for multi-cell distributed multiple-input multiple-output (MIMO) systems is addressed in this paper. We explore this issue by introducing discriminatory treatment of different channel parameters. Generally, the large-scale channel state information (CSI) is predictable and could be regarded as priori information in pilot design, due to its slowly-varying characteristics. In particular, by assuming the large-scale CSI is known a priori, we derive a lower bound on the achievable sum rate in the downlink with a linear detector at the mobile terminal (MT), taking both intercell interference and channel estimation error into account. The problem of pilot sequence design is first formulated, of which the target is to maximize the lower bound of the achievable sum rate with a total pilot power constraint for each cell. Afterwards, we solve the problem by introducing the iterative concave-convex procedure (CCCP) with the demonstration of its convergence. Simulation results illustrate the validity and superiority of the proposed pilot design scheme.

With the ever-increasing demands for local area services of popular content sharing, device-to-device (D2D) communication is conceived to be a key component for the next-generation cellular networks. D2D communications consume low energy and enhance system capacity via proximity range transmissions, where multi-hop transmissions are needed for real-time content sharing. Aiming to solve the challenging problem for multi-hop D2D communication networks, existing works only consider physical domain information to create Long-range Links (LLs) to reduce the transmission delay of multi-hop. In contrast, in this paper, we propose social community aware LLs establishment strategy, which exploits the interplay between social network's features and physical domain constrains. We first formulate the LLs establishment strategy as a cost optimization problem, then propose an efficient optimal greedy algorithm to solve the formulated problem, which is suitable for scenarios of both single sink and distributed content sharing. Numerical results demonstrate that our proposed solution decreases the average path length significantly compared with other state-of-the-art schemes.

The integrated and/or hybrid satellite and terrestrial network has become more and more important because of its broad application prospect and has received considerable attention. At the same time, the integrated network also brings many challenges, especially the problem of interference. Due to the lack of frequency spectrum, frequency reuse is considered in the satellite network and the terrestrial network for enhancing spectral efficiency. However, this will cause considerable Co-Channel Interference (CCI) and thus interference coordination is imperative. In this paper, we propose an interference coordination scheme for the integrated satellite and terrestrial network. The satellite sends pilots for channel estimation at terrestrial base-stations, and transmits the received data to the terrestrial gateway. Then interference coordination is performed at the terrestrial gateway, where the interference channel is updated according to both the estimated information and the predicted change based on the positions. Furthermore, based on the scheme, we analyze the precision that needs to be reached and obtain a direct view on how the precision may influence the system performance.

In this paper, we focus on a high performance compression scheme for remote-sensing images, which is essential due to limited transmission bandwidth while explosively growing remote-sensing image data size. First, on the basis of intra-image spatial redundancy removal, which is used by JPEG 2000 and CCSDS, priori-information is introduced to eliminate temporal redundancy between historical and newly-captured images, at the same time. Second, feature registration technique is applied rather than motion estimation and compensation which is used in HEVC, to deal with the long-range non-linear correlation of remote-sensing image series. Numerical simulation results show that the proposed scheme outperforms JPEG 2000 and JPEG by over 1.37 times for lossless compression, and presents a 5 dB PSNR gain over JPEG 2000 and HEVC for lossy compression.

This paper presents an On-line Gaussian Elimination (OGE) decoding algorithm for 3GPP MBMS Raptor, which can fundamentally shorten the decoding time. As we know, in Gaussian Elimination based Raptor decoding algorithm, calculations start after all data needed have been received. While in the proposed OGE algorithm, decoding calculations are advanced to the moment that only several encoded symbols have been received. By doing so, most of decoding work could be finished during receiving stage and only a small amount of post-processing needs to be done after receiving. Therefore, the period usually used for decoding could be shortened dramatically. Simulation results show that for all kinds of decoding scenarios, OGE algorithm could always shorten the decoding time to less than 1% that of 3GPP Raptor standard decoding algorithm while maintaining the same decoding performance.

Coordinated multi-point(CoMP) transmission clustering schemes could provide significant gains of system performance, such as throughput and cell- edge user data rates. Due to limitations of the backhaul communication and signal processing capability of base stations(BSs), the intrinsic problem of CoMP is that the selection of which BSs shall cooperate as only a few of BSs can be grouped in a cluster. However, approximating the theoretical performance bound of this clustering problem in CoMP at present is seldom discussed due to its inherent combinatorial complexity. In this paper, a novel efficient multi-cell clustering scheme based on blossom tree algorithm is proposed for cellular networks, incorporating CoMP with two cells in each cluster. With blossom tree algorithm, the proposed scheme can find out the optimal clustering strategy and help the CoMP transmission reach its theoretical performance bound on data rate in real-time computing(milliseconds in MATLAB simulation for one clustering). The simulation results show that our proposed method outperforms the existing dynamic greedy method in terms of cell edge users' average achievable data rate. Besides, it can also maintain high performance when extended to larger clusters in that with 4-cell clustering, the proposed method can reach 23.8% higher data rates than dynamic greedy method.

We investigate Tx/Rx joint beamforming in millimeter-wave communications (MMWC). As the multipath components (MPCs) have different steering angles and independent fadings, beamforming aims at achieving array gain and diversity gain in this scenario. A sub-optimal beamforming scheme is proposed to find the antenna weight vectors (AWVs) at Tx/Rx via iterative eigenvalue decomposition (EVD), provided that full channel state information (CSI) is available at both the transmitter and receiver. To make this scheme practically feasible in MMWC, a corresponding training approach is suggested to avoid the channel estimation and iterative EVD computation. As in fast fading scenario, the training approach may be time-consuming due to frequent training; another beamforming scheme, which exploits the quasi-static steering angles in MMWC, is proposed to reduce the overhead and increase the system reliability by multipath grouping (MPG). The scheme first groups the MPCs and then concurrently beamforms toward multiple steering angles of the grouped MPCs, so that both array gain and diversity gain are achieved. Performance comparisons show that, compared with the corresponding state-of-the-art schemes, the iterative EVD scheme with the training approach achieves the same performance with a reduced overhead and complexity, whereas the MPG scheme achieves better performance with approximately equivalent complexity.

The ever-increasing demands for local area services underlaying cellular networks benefit from direct device-to-device (D2D) communications, where an efficient scheme for resource allocation is needed to increase the system capacity as the result of interference caused by spectrum sharing. Current works mainly focus on maximizing the overall transmission capacity according to interference constraints of the physical domain. However, D2D users in the social domain form different social communities, and each social community is likely to improve its own group's data transmission cooperatively without considering other communities. Therefore, social relationships among mobile users influence the strategy of the resource allocations for the D2D communications. In this paper, we first introduce social relationships in the continuum space into the resource allocation for D2D communications, which consider the complex social connections in the social domain. Then a social group utility maximization game is formulated to maximize the social group utility of each D2D user, which quantitatively measures the joint performance of social and physical domains. We theoretically investigate the Nash Equilibrium of our proposed game and further propose a distributed algorithm based on the switch operations of the resource allocation vector. Numerical results demonstrate that our proposed solution increases the utility of overall social groups about 45% on average without loss of the fairness compared with other state-of-the-art schemes.

In the downlink transmission system with a multi-antenna base station (BS) and a cluster of single-antenna mobile stations (MSs), the throughput can be greatly enhanced by exploiting the cooperating channel among MSs and forming virtual multiple input and multiple output (MIMO). With MSs close together, compress-and-forward is the most commonly used cooperation strategy. Considering the fact that in practical systems the transmit signal is usually finite-alphabet modulated symbols, e.g., quadrature amplitude modulation (QAM), the compress-and-forward cooperation is investigated correspondingly. Regarding the bit error rate (BER) performance of virtual MIMO systems, two regions are identified: the channel-noise-dominant region and the compression-noise-dominant region. In the channel-noise-dominant region, the cooperation system yields almost no loss compared to the BER lower bound. While in the compression-noise-dominant region, the system BER is limited, i.e., with an error floor. Through theoretical analysis, the minimum compression rate to guarantee that the system works in the channel-noise-dominant region is obtained. The minimum compression rate is determined mainly by three terms: the modulation alphabet size, the constellation position and the signal-to-noise ratio (SNR), whose explicit expression has also been derived.

In the implementation of distributed antenna systems (DASs), the antenna elements in some cases may only be deployed in the form of distributed antenna-clusters (ACs), due to various practical limitations. Consequently, correlation usually exists among the antenna elements within each AC. In contrast to most of the previous work that focused on the antenna correlation in a single-cell environment, this paper investigates the impact of the antenna correlation in a more general multi-cell scenario, where the inter-cell interference becomes the key challenge. We formulate a joint multi-cell input covariance optimization problem, accounting for the transmit antenna correlation, the propagation path-loss and the shadow fading. We show that the problem is a complicated non-convex problem. Moreover, the objective function, i.e., the ergodic sum capacity, is found difficult to be expressed in a straightforward form. After mathematical simplification, we propose an iterative inter-cell coordination scheme by adopting the successive approximation method. Simulation results demonstrate that, thanks to much more effective adaptation to the antenna correlation, the proposed scheme outperforms the existing ones and can significantly improve the system performance of a DAS with highly-correlated ACs.

This paper investigates QoS-aware relay selection and subcarrier assignment in cooperative OFDMA networks. In contrast to some existing works, which solve the sum-rate maximization problem directly, we first simplify it by exploiting the structure of the optimal solution, then we solve the simplified problem. To characterize the optimal network sum-rate performance, we solve the simplified problem optimally by the branch-and-cut method. In addition to the sum-rate maximization problem, we demonstrate that its two variants can also be similarly simplified and then be solved optimally by the branch-and-cut method. The optimal method serving as the benchmark is particularly suitable for moderate scale problems. Simulations show that the branch-and-cut method can locate the optimal solution effectively, which may in turn provide some insights into the performance of the heuristic method.

Materials play a critical role in memristive devices and the research community is aggressively searching for the most applicable material systems for memristive switching. Two representative examples of newly developed switching materials are presented in this paper, including nitride memristors and Pt doped SiO2nanometallic memristors. The former represents nonoxide systems that might be more compatible with nitride electrodes preferred in a fab and the latter represents engineered materials that exhibit a better controllability over the formation of switching channel(s).

It has been long believed that the turbo equalizer leveraging a soft input/soft output (SISO) detector is effective for system performance enhancement in terms of bit error rate (BER). In practical applications, the implementation of SISO detection is usually challenging, due to its high computational-complexity. To address this issue, this paper proposes a low complexity SISO detector based on QR decomposition (QRD) and the M-search algorithm for single carrier block transmission systems. Benefiting from two unique properties called the aggregation property and the natural ordering property obtained by applying the QRD method into single carrier block transmission systems, the QRD-M based SISO detection algorithm can be dramatically simplified. Detailed analysis shows a linear growing computational-complexity. The extrinsic information transfer (EXIT) chart analysis tool is used to illustrate the performance of the proposed scheme. Both EXIT analysis and simulation results reveal that the turbo equalization with the proposed QRD-M detection algorithm could achieve a sub-optimal system performance close to the BER-optimal maximum a posteriori (MAP) detector at each iteration with a much lower computational-complexity.

The quasi-cyclic low-density parity-check (QC-LDPC) codes have attracted much attention in space communication systems. However, the decoders are still difficult to be applied in practice for their large area and high memory requirements. Moreover, the clock cycles for the input and output interfaces can not be ignored due to the I/O resource is also limited on this occasion, which influences the throughput improvement significantly. This paper presents a parallel pipelined decoder architecture for QC-LDPC codes, which can largely reduce their area and memory size while maintaining a fast convergence speed. The decoding approach reformulates the original normalized min-sum algorithm and adopts a two pipelines architecture to eliminate the clock cycles for the I/O and reduce the number of clock cycles per iteration. Chip designed with TSMC 0.13-µm eight-metal-layer standard CMOS technology shows that it can achieve a throughput up to 767 Mbps with only 3.12 mm2core area consumption. Especially, only 36 I/O ports are occupied, less than 10% of the conventional decoders.

In indoor UWB TDoA localization systems, anchor clock drifts, RF and ADCs front end delay and their long term variation are main performance limiting factors. Anchor synchronization using reference tags instead of wired lines are promising for ease of deployment In this work, an error analysis on UWB TDoA localization system with reference tag is performed, focusing on propagation and clock drifts. A 500 MHz experimental UWB localization system with reference tag was built. Using single carrier UWB signal with a 127 chip PN code, the smallest TDoA measurement error is 21.7 cm and the worst error is 45.3 cm, as was shown by the test results.

While channel state information (CSI) at the transmitter is critical to the system capacity of multiple input multiple output (MIMO) broadcast channels, its acquisition is practically intricate. This paper offers an alternative and investigates a special MIMO broadcast channel, which exploits the benefit of receiver cooperation and renders it unnecessary to acquire CSI at the transmitter. The cooperation gain is studied in a system-wide perspective by taking into account the resource (power and bandwidth) consumed to establish the cooperation links. Built on that, the generalized cooperation spectral efficiency is defined.With a dedicated approximation, a near optimal resource allocation strategy is presented to maximize the cooperation spectral efficiency. Specially, the power allocation problem is simplified into a polynomial rooting problem. Simulation results show that the receiver cooperation can provide a performance gain and achieve the MIMO capacity. Moreover, performance loss incurred by the proposed method is negligible compared to the numerical exhaustive search scheme.

The frequency selective fading channels cause severe inter-symbol interference (ISI) and significantly degrade the bit error rate (BER) performance of broadband wireless communication systems. In this paper, a soft detection algorithm employing the idea of QR decomposition, data grouping and successive interference cancelation (SIC) is proposed for coded single carrier (SC) block transmission. We show that SC block transmission with unique word (UW) brings two unique features while employing the QRD detection method. We refer to them as the natural ordering property and the sparse property, respectively. The data block is divided into small groups and the log-likelihood ratio (LLR) of each element is derived from the bottom up. A flexible tradeoff between BER performance and detection complexity can be provided. Simulation results show that in frequency selective fading channels, the proposed scheme can obtain dramatic performance gain compared to the minimum mean square error (MMSE) single carrier frequency domain equalization(FDE) with low complexity.

In millimeter-wave communications (MMWC), multipath components (MPCs) have different steering angles and independent fadings. Park and Pan recently proposed a simple scheme for both transmitter and receiver to concurrently beamform towards multiple steering angles of MPCs to achieve both array gain and diversity gain. However, when the number of MPCs is greater than that of the transmit or receive antennas, a solution of antenna weight vector (AWV) does not exist. To cope with this problem, two multipath grouping (MPG) schemes, namely MPG-steering vector grouping and MPG-channel vector grouping, are proposed. These schemes group the MPCs that have close steering angles, and define an equivalent MPC for each non-empty group in both the transmitter and receiver. As the number of non-empty groups is always no larger than that of antennas in both the transmitter and receiver, a solution of AWV is guaranteed. Moreover, performance comparisons show that the two proposed MPG schemes achieve not only full diversity, but also an even better array gain than that of the scheme proposed by Park and Pan.

Paired Carrier Multiple Access (PCMA) is widely used in bandwidth limited satellite network systems for its high frequency efficiency and compatibility with existing communication methods. With blind detection for PCMA signal being an important topic, various blind detection methods for PCMA signals with specific properties have been proposed. However, information theory analysis is still required for general blind detection method design. This paper introduces information theoretical bound for blind detection using a simulation based computation method, and applies a Viterbi detection method to verify the bound. Given a PCMA signal, the information theoretical bound helps to evaluate whether blind detection is possible, and guides how to design specific blind detection methods efficiently. Simulation result shows how mutual information carried by PCMA signal of the communicating peers is influenced by signal fading, propagation delay and other parameters numerically.

In this paper, a novel spread dimension (SD) communication using orthogonal patterns to transmit information is proposed to achieve the reliable communication with the smarty jamming. A soft demodulator is presented and a posterior probability (APP) according to maximum a posterior probability (MAP) estimation is derived to obtain the soft information for soft iterative decoding. The demodulator is suitable for coherent reception ,non-coherent reception with the channel state information (CSI) and non-coherent reception without CSI. Besides, the capacity of the coded modulation scheme which employs GF(q) LDPC codes is compared with the bit interleaved coded modulation (BICM) scheme using the binary LDPC codes for the SD communication system. We prove that the CM scheme is able to better utilize the soft information and owns a higher achievable rate. Simulation results show that the gain of the q-ary LDPC coded SD system is up to 0.5 dB over AWGN channel and has a very good performance with the smarty jamming.

The virtual multiple input multiple output (MIMO) technique can dramatically improve the performance of a multi-cell distributed antenna system (DAS), thanks to its great potentials for inter-cell interference mitigation. One of the most challenging issues for virtual MIMO is the acquisition of channel state information at the transmitter (CSIT), which usually leads to an overwhelming amount of system overhead. In this work, we focus on the case that only the slowly-varying large-scale channel state is required at the transmitter, and explore the performance gain that can be achieved by coordinated transmissions for virtual MIMO with large-scale CSIT. Aiming at maximizing the achievable ergodic sum rate, the input covariances for all the mobile terminals (MTs) are jointly optimized, which turns out to be a complicated non-convex problem with a non-closed-form objective function. Further analysis reveals that the coordinated transmission problem can be recast as a Max-Min problem with a closed-form objective function and linear constraints. Then, by appealing to the successive approximation method and the saddle-point theory of concave-convex functions, we propose an iterative algorithm for coordinated transmissions with large-scale CSIT and establish its convergence. Simulation results corroborate that the proposed scheme converges quickly, and it yields significant performance gains compared to the existing schemes. Moreover, it is observed that the proposed scheme can achieve a nearly globally-optimal point under the diagonal input covariance constraint. Since the acquisition of large-scale CSIT is far less demanding than that of full CSIT, we believe that the proposed coordinated transmissions with large-scale CSIT in DASs shed some light on virtual MIMO in the making.

Rapid detection of the arrival of a packet is challenging in burst wireless communications, where many parameters are unknown, such as signal power, noise power and carrier phase offset. Due to the unknown noise power, it is hard to set appropriate thresholds for the existing common detectors under the Neyman-Pearson criterion, which results in vulnerable acquisition and poor performance. In order to solve this problem, we propose the generalized likelihood-ratio test (GLRT) approach for sequence-aided packet acquisition in this paper. GLRT detection is formulated under multipath channel. Moreover, false alarm probability and detection probability of GLRT are derived and confirmed via simulations. Comparisons are conducted between GLRT and the common detectors, as well as GLRT under different channels. Results show that GLRT basically reveals more competitive and robust performance than the common detectors in practice, with only a little extra hardware cost by using the provided recursive computation structure. Additionally, it is shown that fading results in significant deterioration for GLRT; whereas GLRT has ability to exploit multipath diversity to reduce fading effect and improve acquisition performance.

The time-frequency interleave (TFI) diversity scheme for single carrier block transmission with frequency domain equalization (SC-FDE) has been proposed. For single antenna wireless communication systems, it can provide frequency diversity in frequency selective channels by interleaving and retransmitting. In this article, the iterative block decision feedback equalization (IBDFE) structure for TFI diversity scheme is proposed. Both the feed-forward filter (FFF) and feed-backward filter (FBF) work in the frequency domain, which makes the implementation complexity low. The FFF and FBF coefficients based on minimum mean square error (MMSE) criterion are derived. The correlation between the transmitted symbols and the detected symbols are estimated in a simple iterative manner. Simulation results show that for TFI diversity scheme with IBDFE, a much better bit error rate (BER) performance can be obtained than TFI with frequency domain linear equalizer (FD-LE) only for one more iteration. Compared to IBDFE without TFI diversity, the performance gain is impressive.

In the Wireless Body Area Network (WBAN), the wireless channel is complex and distinctive because of the irregular shape of human body. The channel modeling methods and results are different from those in traditional narrow-band communication environments. In this paper, we present channel models for WBAN in Ultra wide band (UWB) frequency range 3-9 GHz. The channels are modeled statistically and the channel model parameters are derived from actual measured data in an office environment. An interesting common result is that different shape people have significantly differences in multipath parameters. Taking into account the characteristics of body shape, we provide a new classified small-scale channel model which has three kind of channel models: sparse, medium and dense multipath channel models. These models can be applied to different scenarios and improve the system design. We evaluated these models by delays and average number of mUltipaths with the measured data. Results prove the effectiveness of our models.

Traditional Delay-Locked-Loop (DLL) method can be hardly used in high-rate Direct-Sequence Ultra-Wideband (DS-UWB) systems due to dense multipath environment, low spreading factor and high-frequency clock. Most of the existing researches on tracking in UWB scenario focus on low-rate Impulse-Radio UWB (IR-UWB). In this paper, a non-Numerical Controlled Oscillator (NCO) periodical-pilot-assisted tracking method is proposed for practical high-rate DS-UWB systems. Analytical expressions of Equivalent Probability Density Function (EPDF) of timing error in locked state, Mean Square Error (MSE, or termed timing jitter) and Mean Time to Lose Lock (MTLL) are derived. Numerical results are presented, and then confirmed by means of extensive computer simulation results. These results corroborate our theoretical analysis, and show performance superiority of the proposed method over traditional DLL. Additionally, this proposed method has been used and justified in our practical DS-UWB system.

This paper analyzes the interference in Direct Sequence Spreading Spectrum (DSSS) systems with Selective RAKE (SRAKE) receivers where channel delay is comparable with spreading sequence length. We build an accurate output model of SRAKE receiver and derive the output Signal to Interference-Noise Ratio (SINR) and Bit Error Rate (BER) of SRAKE receiver, taking multipath effect into consideration. Based on this analysis, we showed that in frequency selective channels with SRAKE receiver, system become interference limited as transmit power increases. Besides, there exists a critical spreading factor. When spreading factor is lower than this critical value, Eb/N0have to increase dramatically to compensate the multipath effect. This provide a criterion to adaptively choose the optimal spreading factor under different channel conditions.

This paper presents an approach for analyzing the key parts of a general digital radio frequency (RF) charge sampling mixer based on discrete-time charge values. The cascade sampling and filtering stages are analyzed and expressed in theoretical formulae. The effects of a pseudo-differential structure and CMOS switch-on resistances on the transfer function are addressed in detail. The DC-gain is restrained by using the pseudo-differential structure. The transfer gain is reduced because of the charge-sharing time constant when taking CMOS switch-on resistances into account. The unfolded transfer gains of a typical digital RF charge sampling mixer are analyzed in different cases using this approach. A circuit-level model of the typical mixer is then constructed and simulated in Cadence SpectreRF to verify the results. This work informs the design of charge-sampling, infinite impulse response (IIR) filtering, and finite impulse response (FIR) filtering circuits. The discrete-time approach can also be applied to other multi-rate receiver systems based on charge sampling techniques.

A clock and data recovery (CDR) circuit is proposed based on the sigma-delta quantization. The phase of the new CDR circuit is adjusted by a sigma-delta modulated reference clock that increases the stability of the system and can easily interface with PLL cores embedded in FPGAs. The approximate linear model of the proposed CDR is analyzed for SONET/SDH applications to evaluate its performance. The measurement shows that the jitter tolerance meets the ITU-T requirement with a high margin of 0.3UI. The commercial equipment has been developed using a single FPGA chip based on the SDM-CDR